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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6

Impact damage resistance of composite fuselage structure, part 2

The strength of laminated composite materials may be significantly reduced by foreign object impact induced damage. An understanding of the damage state is required in order to predict the behavior of structure under operational loads or to optimize the structural configuration. Types of damage typically induced in laminated materials during an impact event include transverse matrix cracking, delamination, and/or fiber breakage. The details of the damage state and its influence on structural behavior depend on the location of the impact. Damage in the skin may act as a soft inclusion or affect panel stability, while damage occurring over a stiffener may include debonding of the stiffener flange from the skin. An experiment to characterize impact damage resistance of fuselage structure as a function of structural configuration and impact threat was performed. A wide range of variables associated with aircraft fuselage structure such as material type and stiffener geometry (termed, intrinsic variables) and variables related to the operating environment such as impactor mass and diameter (termed, extrinsic variables) were studied using a statistically based design-of-experiments technique. The experimental design resulted in thirty-two different 3-stiffener panels. These configured panels were impacted in various locations with a number of impactor configurations, weights, and energies. The results obtained from an examination of impacts in the skin midbay and hail simulation impacts are documented. The current discussion is a continuation of that work with a focus on nondiscrete characterization of the midbay hail simulation impacts and discrete characterization of impact damage for impacts over the stiffener.

Dost, Ernest F.↗

Statistical Engineering in Air Traffic Management Research

NASA is working to develop an integrated set of advanced technologies to enable efficient arrival operations in high-density terminal airspace for the Next Generation Air Transportation System. This integrated arrival solution is being validated and verified in laboratories and transitioned to a field prototype for an operational demonstration at a major U.S. airport. Within NASA, this is a collaborative effort between Ames and Langley Research Centers involving a multi-year iterative experimentation process. Designing and analyzing a series of sequential batch computer simulations and human-in-the-loop experiments across multiple facilities and simulation environments involves a number of statistical challenges. Experiments conducted in separate laboratories typically have different limitations and constraints, and can take different approaches with respect to the fundamental principles of statistical design of experiments. This often makes it difficult to compare results from multiple experiments and incorporate findings into the next experiment in the series. A statistical engineering approach is being employed within this project to support risk-informed decision making and maximize the knowledge gained within the available resources. This presentation describes a statistical engineering case study from NASA, highlights statistical challenges, and discusses areas where existing statistical methodology is adapted and extended.

Wilson, Sara R.↗

Integrated design and manufacturing for the high speed civil transport (a combined aerodynamics/propulsion optimization study)

This report documents the efforts of a Georgia Tech High Speed Civil Transport (HSCT) aerospace student design team in completing a design methodology demonstration under NASA's Advanced Design Program (ADP). Aerodynamic and propulsion analyses are integrated into the synthesis code FLOPS in order to improve its prediction accuracy. Executing the integrated product and process development (IPPD) methodology proposed at the Aerospace Systems Design Laboratory (ASDL), an improved sizing process is described followed by a combined aero-propulsion optimization, where the objective function, average yield per revenue passenger mile ($/RPM), is constrained by flight stability, noise, approach speed, and field length restrictions. Primary goals include successful demonstration of the application of the response surface methodolgy (RSM) to parameter design, introduction to higher fidelity disciplinary analysis than normally feasible at the conceptual and early preliminary level, and investigations of relationships between aerodynamic and propulsion design parameters and their effect on the objective function, $/RPM. A unique approach to aircraft synthesis is developed in which statistical methods, specifically design of experiments and the RSM, are used to more efficiently search the design space for optimum configurations. In particular, two uses of these techniques are demonstrated. First, response model equations are formed which represent complex analysis in the form of a regression polynomial. Next, a second regression equation is constructed, not for modeling purposes, but instead for the purpose of optimization at the system level. Such an optimization problem with the given tools normally would be difficult due to the need for hard connections between the various complex codes involved. The statistical methodology presents an alternative and is demonstrated via an example of aerodynamic modeling and planform optimization for a HSCT.

Baecher, Juergen↗

Auxiliary Inlet Design Study for Mach 1.4

Auxiliary inlet configurations were developed for inlets designed for a NASA Mach 1.4 Supersonic Technology Concept Aeroplane (STCA). The inlets included axisymmetric pitot and axisymmetric spike inlets. The auxiliary inlets were full circumferential slots within the cowl. Computational fluid dynamics (CFD) simulations were performed to obtain inlet performance metrics of total pressure recovery and radial distortion at the engine face for take-off and approach conditions. Methods of design of experiments were used to explore the statistical significance of the design factors for the auxiliary inlets which included the axial location, length of the opening, and auxiliary inlet angle. The results demonstrate the effect of the design factors and show the potential for improved inlet performance with the use of auxiliary inlets.

supersonic inlets↗

Plant growth modeling at the JSC variable pressure growth chamber - An application of experimental design

This paper describes the approach and results of an effort to characterize plant growth under various environmental conditions at the Johnson Space Center variable pressure growth chamber. Using a field of applied mathematics and statistics known as design of experiments (DOE), we developed a test plan for varying environmental parameters during a lettuce growth experiment. The test plan was developed using a Box-Behnken approach to DOE. As a result of the experimental runs, we have developed empirical models of both the transpiration process and carbon dioxide assimilation for Waldman's Green lettuce over specified ranges of environmental parameters including carbon dioxide concentration, light intensity, dew-point temperature, and air velocity. This model also predicts transpiration and carbon dioxide assimilation for different ages of the plant canopy.

Miller, Adam M.↗

System Synthesis in Preliminary Aircraft Design using Statistical Methods

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).

DeLaurentis, Daniel↗

System Synthesis in Preliminary Aircraft Design Using Statistical Methods

This paper documents an approach to conceptual and early preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically Design of Experiments (DOE) and Response Surface Methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an Overall Evaluation Criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in an innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting in solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a High Speed Civil Transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabilistic designs (and eventually robust ones).

DeLaurentis, Daniel↗

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment using Design of Experiments Methodology

This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to pyroshock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to pyroshock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite pyroshock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to pyroshock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and pyroshock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to pyroshock induced loading.

Parsons, David S.↗

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment Using Design of Experiments Methodology

This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to pyroshock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to pyroshock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite pyroshock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to pyroshock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and pyroshock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to pyroshock induced loading.

Parsons, David S.↗

Characterizing the Response of Composite Panels to a Pyroshock Induced Environment Using Design of Experiments Methodology

This experimental study seeks to quantify the impact various composite parameters have on the structural response of a composite structure in a pyroshock environment. The prediction of an aerospace structure's response to shock induced loading is largely dependent on empirical databases created from collections of development and flight test data. While there is significant structural response data due to shock induced loading for metallic structures, there is much less data available for composite structures. One challenge of developing a composite shock response database as well as empirical prediction methods for composite structures is the large number of parameters associated with composite materials. This experimental study uses data from a test series planned using design of experiments (DOE) methods. Statistical analysis methods are then used to identify which composite material parameters most greatly influence a flat composite panel's structural response to shock induced loading. The parameters considered are panel thickness, type of ply, ply orientation, and shock level induced into the panel. The results of this test will aid in future large scale testing by eliminating insignificant parameters as well as aid in the development of empirical scaling methods for composite structures' response to shock induced loading.

Parsons, David↗

Statistical Methodologies to Integrate Experimental and Computational Research

Development of advanced algorithms for simulating engine flow paths requires the integration of fundamental experiments with the validation of enhanced mathematical models. In this paper, we provide an overview of statistical methods to strategically and efficiently conduct experiments and computational model refinement. Moreover, the integration of experimental and computational research efforts is emphasized. With a statistical engineering perspective, scientific and engineering expertise is combined with statistical sciences to gain deeper insights into experimental phenomenon and code development performance; supporting the overall research objectives. The particular statistical methods discussed are design of experiments, response surface methodology, and uncertainty analysis and planning. Their application is illustrated with a coaxial free jet experiment and a turbulence model refinement investigation. Our goal is to provide an overview, focusing on concepts rather than practice, to demonstrate the benefits of using statistical methods in research and development, thereby encouraging their broader and more systematic application.

Parker, P. A.↗

Developing and applying quantifiable metrics for diagnostic and experiment design on Z

This project applies methods in Bayesian inference and modern statistical methods to quantify the value of new experimental data, in the form of new or modified diagnostic configurations and/or experiment designs. We demonstrate experiment design methods that can be used to identify the highest priority diagnostic improvements or experimental data to obtain in order to reduce uncertainties on critical inferred experimental quantities and select the best course of action to distinguish between competing physical models. Bayesian statistics and information theory provide the foundation for developing the necessary metrics, using two high impact experimental platforms on Z as exemplars to develop and illustrate the technique. We emphasize that the general methodology is extensible to new diagnostics (provided synthetic models are available), as well as additional platforms. We also discuss initial scoping of additional applications that began development in the last year of this LDRD.

97 MATHEMATICS AND COMPUTING↗

ATS-F Comsat Millimeter Wave Propagation Experiment

The ATS-F Comsat Millimeter Wave Propagation Experiment has been designed to gather statistical data on the attenuation caused by rain at millimeter wave frequencies. These data will be used to determine system design parameters for future communications satellite systems operating at frequencies above 10 GHz. The experiment has 39 ground terminals transmitting at 13.2 or 17.8 GHz to a transponder on board the ATS-F satellite. The transponder retransmits these signals at 4 GHz to a central earth terminal which records their amplitudes once each second. The data will be analyzed to provide probabilities of attenuation as functions of parameters such as rainfall, location, and time. These probabilities can then be used to determine the required power margins of millimeter wave communications systems. Techniques of overcoming severe attenuation such as site diversity and the use of a spot beam to increase the power level at selected locations will also be evaluated.

Westerlund, L. H.↗

Using statistical modeling to predict and understand fusion experiments

Over 300 cryogenic layered direct-drive inertial confinement fusion implosions have been successfully executed on the OMEGA Laser System in the last decade. However, extracting sufficient understanding from these experiments to develop new designs or to identify or mitigate degradation sources remains challenging. Recently, a statistical modeling approach was developed to successfully design and predict improved implosion experiments on OMEGA. Here, we show that one-dimensional simulations can be used to predict the outcomes of systematically perturbed three-dimensional simulations and that this statistical modeling approach can be used to identify or rule out physical mechanisms for some of the degradation sources observed on the OMEGA Laser System for direct-drive cryogenic inertial confinement fusion. In this instance, we investigate the fusion yield dependencies on the ion temperature asymmetries and laser beam size observed in experiments by comparing with trends in three-dimensional synthetic simulation databases. Using the statistical model on these systematically perturbed simulations, we find that the statistically inferred dependency on the measured ion temperature asymmetries is well explained by imposed ℓ =1 modes. However, we find that the dependency on the laser beam size is only dominated by the illumination non-uniformity for some extreme cases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Simulating the SuperCDMS Dark Matter Detector Response and Readout

Dark matter is one of the remaining great mysteries of modern physics. Cosmological and astrophysical evidence hints strongly at a new particle, or set of particles, unknown to the Standard Model, comprising some 80% of all of the matter in the universe. SuperCDMS is one of the world-leading direct-detection experiments attempting to directly measure interactions between dark and regular matter. SuperCDMS uses solid state crystalline detectors in a nearly-zero background environment to attempt to measure the particle properties of dark matter. The detectors themselves, however, are quite complex; and a very detailed understanding of the microscopic physics is helpful in analyzing the very rare events that occur within them. Furthermore, better understanding and modeling of the detectors can aid in the design and optimization of future iterations of the experiment. The SuperCDMS detectors are well-understood in ideal cases. However, an accurate understanding of the detector response to outlier events or under non-ideal conditions is important for a low-statistics experiment. This work describes the design and implementation of a low temperature condensed matter physics simulation library built on top of the popular Geant4 particle tracking framework. The library, named \Geant4 Condensed Matter Physics" or G4CMP, introduces several solid state concepts to the Geant4 framework such as crystal lattices, phonon quasiparticles, non-scalar elective masses, and implements several physics processes relevant to cryogenic temperature crystals. In addition to the physics library, which is intended for general use, this work also describes a full Monte Carlo simulation package for the SuperCDMS iZIP detectors which utilizes G4CMP at its core and also fully simulates the detector readout sensors. The document will begin with the canonical motivations for the cold dark matter hypothesis, followed by a description of the SuperCDMS experiment. It will then describe the microscopic physics inside of the SuperCDMS detectors, as well as details on their implementation in G4CMP. The full detector simulation with corresponding sensor readout will follow, along with plans for future work; including both scientific studies and improvements, as well as computational improvements and added efficiencies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Understanding Process–Structure Relationships during Lamination of Halide Perovskite Interfaces

Fabrication of halide perovskite (HP) solar cells typically involves the sequential deposition of multiple layers to create a device stack, which is limited by the thermal and chemical incompatibility of top contact layers with the underlying HP semiconductor. One emerging strategy to overcome these restrictions on material selection and processing conditions is lamination, where two half-stacks are independently processed and then diffusion bonded to complete the device. Lamination reduces the processing constraints on the top side of the solar cell to allow new device designs, expanded use of deposition methods, and self-encapsulation of devices. While laminated perovskite solar cells with high efficiencies and novel interlayer combinations have been demonstrated, there is a limited understanding of how the lamination process parameters affect the diffusion-bond quality and material properties of the resulting HP layer. In this study, we systematically vary temperature, pressure, and time during lamination and quantify the resulting impacts on bonded area, grain domain size, and photoluminescence. A design of experiments is performed, and statistical analysis of the experimental results is used to quantitatively evaluate the resulting process–structure–property relationships. The lamination temperature is found to be the key parameter controlling these properties. Furthermore, a temperature of 150 °C enables successful bonding over 95% of the substrate area and also results in increases in apparent grain domain size and photoluminescence intensity. Based on these insights, the lamination temperature of functional perovskite solar cell devices is varied, demonstrating the importance of the resulting bond quality on device performance metrics.

14 SOLAR ENERGY↗

Design optimization for cost and quality: The robust design approach

Designing reliable, low cost, and operable space systems has become the key to future space operations. Designing high quality space systems at low cost is an economic and technological challenge to the designer. A systematic and efficient way to meet this challenge is a new method of design optimization for performance, quality, and cost, called Robust Design. Robust Design is an approach for design optimization. It consists of: making system performance insensitive to material and subsystem variation, thus allowing the use of less costly materials and components; making designs less sensitive to the variations in the operating environment, thus improving reliability and reducing operating costs; and using a new structured development process so that engineering time is used most productively. The objective in Robust Design is to select the best combination of controllable design parameters so that the system is most robust to uncontrollable noise factors. The robust design methodology uses a mathematical tool called an orthogonal array, from design of experiments theory, to study a large number of decision variables with a significantly small number of experiments. Robust design also uses a statistical measure of performance, called a signal-to-noise ratio, from electrical control theory, to evaluate the level of performance and the effect of noise factors. The purpose is to investigate the Robust Design methodology for improving quality and cost, demonstrate its application by the use of an example, and suggest its use as an integral part of space system design process.

Unal, Resit↗